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On correlation analysis of many-to-many observations: an alternative to Pearson's correlation coefficient and its application to an ecotoxicological study

机译:关于多对多观测值的相关分析:皮尔逊相关系数的替代方法及其在生态毒理学研究中的应用

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Correlation studies are an important hypothesis-generating and testing tool, and have a wide range of applications in many scientific fields. In ecological studies in particular, multiple environmental variables are often measured in an attempt to determine relationships between chemical, physical and biological factors. For example, one may wish to know whether and how soil properties correlate with plant physiology. Although correlation coefficients are widely used, their properties and limitations are often imperfectly understood. This is especially the case when one is interested in correlations between, say, trace element content in sediments and in marine organisms, where no one-to-one correspondence exists. We show that evaluating Pearson's correlation coefficient for either site-specific means or composite samples results in biased estimates, and we propose an alternative estimator. We use simulation studies to demonstrate that our estimator generally has a much smaller bias and mean squared error. We further illustrate its use in a case study of the correlation between trace element content in sediments and in mussels in Lyttelton Harbour, New Zealand.
机译:相关性研究是重要的假设生成和检验工具,在许多科学领域中具有广泛的应用。特别是在生态学研究中,通常会测量多个环境变量,以试图确定化学,物理和生物学因素之间的关系。例如,人们可能想知道土壤特性是否以及如何与植物生理相关。尽管相关系数已被广泛使用,但它们的性质和局限性往往不完善。当人们对沉积物和海洋生物中微量元素含量之间的相关性感兴趣时,尤其如此,其中不存在一对一的对应关系。我们表明,针对特定地点的均值或复合样本评估Pearson相关系数会导致估计偏差,并且我们提出了一种替代估计量。我们使用仿真研究来证明我们的估算器通常具有较小的偏差和均方误差。我们在新西兰利特尔顿港的沉积物中和贻贝中的微量元素含量之间的相关关系的案例研究中进一步说明了其用途。

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